This invention relates to digital imaging techniques. More particularly, the invention relates to an improved image optimization technique for differentially processing pixel values representative of image structure while maintaining overall image quality.
A number of digital imaging techniques are known and presently in use. These range from digital cameras, to sheet or picture scanners, to sophisticated scanners used in fields such as medical diagnostics. Depending upon the system design, digital pixel data is obtained and used to generate useful images.
Magnetic resonance (MR) imaging techniques, for example, subject an object to a uniform magnetic field upon which various gradient fields have been superimposed. The uniform magnetic field homogenizes the spins of responsive material within the object such that the spins are effectively aligned. An excitation if pulse is the applied to “tip” the spins of the responsive material into a plane transverse to the uniform magnetic field. Upon removal of the excitation if pulse, the spins realign with the uniform magnetic field and, in the process, emit a resonance signal. These resonance signals are detected by the imaging system and are processed to produce the magnetic resonance image.
Current MR imaging and certain other systems process the detected resonance signals using reconstruction algorithms with zero-filled interpolation (ZIP) capability, or similar techniques. By using ZIP, the reconstruction algorithms are capable of producing differently sized interpolated images as specified by the operator. However the interpolation changes the image noise characteristics and the extent of point spread, thus affecting image quality.
Typically, image data is filtered to improve image quality and/or enhance selected characteristics. One such type of filtering might differentially enhance specific structures within the image, such as bone, soft tissue, or fluids in the medical diagnostics field, which are relied upon by physicians or radiologists in making their diagnosis. Such structures may be physically defined in the image by contiguous edges, contrast, texture, and so forth. In such structure enhancing filtering, non-structure textural and contrast information must be maintained to allow interpretation of the structures and the surrounding background.
Ideally the filtering technique will perform its functions in a computationally efficient manner, thereby reducing processing time and the associated hardware requirements. However, current image filter frameworks, such as tools used in MR imaging, do not account for the added variability in noise and point spread function due to interpolation and therefore do not optimize either the image quality or the duration of the filtering process. In addition to affecting the image quality and the time associated with the filtering process, resizing the image by interpolation requires tuning filter and display parameters for each new field of view selected. This returning itself takes additional time and creates additional datasets for storage or processing.
There is a need, therefore, for a more computationally efficient technique for enhancing interpolated images which addresses these concerns. Ideally such a technique would be robust in its implementation, allowing it to be used with any number of imaging systems, such as MR or CT imaging systems with few, if any, modifications.